He and his 98 technology staffers were among the first people outside Kaiser alarmed by mounting data problems.

As wait-time transfer forms arrived, UNOS data analysis workers grabbed them to begin verifying that the person who signed was the same person whose records UNOS already had in its 1-terabyte Microsoft SQL Server database, under UC-Davis or UC-San Francisco. They also checked the data on each person that de Belen had entered electronically into UNOS's system via the Web, to see if it matched.

To do these verifications, UNOS ran a custom-built application whose fuzzy logic a way of programming software to evaluate imprecise and partially missing data appraised the likelihood that a patient new to Kaiser was the same one who had been registered at a UC transplant center, Keck says.

When de Belen's entries were complete and matched existing UNOS data, UNOS then transferred any waiting time, down to the second, the patient had previously accrued to his new listing under Kaiser.

When entries didn't match and hundreds didn't UNOS called Kaiser about the discrepancies, or e-mailed a list of them in an encrypted Excel spreadsheet, according to Keck. Until those were resolved, the patient was unregistered. A Kaiser record might have the same name and date of birth as a UC record, but the Social Security number, for example, would be off. Maybe it was a typo. Maybe it was a different person. As final authority on the wait list, UNOS had to be careful, Keck says.

Kaiser was "adding patients and wanted to immediately grant them large amounts of waiting time accumulated at another center," he explains. "It's extremely important that we get that correct." Taking time from the wrong person "would disadvantage them" in the competition for kidneys, he says.

One Kaiser staff member told DMHC that transplant director Dr. Sharon Inokuchi "stepped forward" and "yelled at" UNOS because Kaiser could not find out why patient registrations weren't being transferred. However, Keck counters, "The ball is in their court to provide some documentation of correct information." Inokuchi did not return Baseline's call for an interview.

Finding correct dates for when patients started dialysis was particularly troublesome for Kaiser, Keck recalls. He doesn't know why, but Kaiser's dialysis data for some patients differed from what UC had provided UNOS for the same patients. Perhaps the dates were not in the files Kaiser had, and if Kaiser asked patients themselves, they remembered incorrectly, he offers as one theory.

Time on dialysis is important in calculating a patient's rank in a match run for a kidney. Several studies, including one in 2000 from the University of Michigan that looked at 10 years of data on 73,000 patients, have shown that the longer someone has been on dialysis, the lower his survival rate after transplant. All other things being equal, wait-list patients who have been doing dialysis longer will be ranked closer to the top of the list when a kidney comes up. The dialysis date "has to be correct," Keck says.

Whatever the reason for discrepancies, he says, it wasn't an impossible puzzle: "Just pick up the phone and call the dialysis center."

Keck grew frustrated when Kaiser wouldn't respond for months with corrected data for so many patients. "I've been here 15 years and this is a first for me," he says. Before going into technology in 1992, Keck was a clinical nurse who helped people cope with stress before and after transplants. "The first half of my career was essentially taking care of patients," he says. "If there's a situation that is compromising the opportunity for a patient to get an organ, it's personally distressing to me."

Senior WriterKim_Nash@ziffdavisenterprise.comKim has covered the business of technology for 14 years, doing investigative work and writing about legal issues in the industry, including Microsoft Corp.'s antitrust trial. She has won numerous awards and has a B.S. degree in journalism from Boston University.